Controller description: ToVo2

A Sarsa-UCT algorithm, using TD backups and space-local Q-value normalization. Builds a tree, does not compute transpositions. Most primitive opponent model: random player, assumed as part of the environment – keeping value estimates only for own agent. No expert knowledge, no handcrafted strategies, no heuristics.

Controller description: not2048

Powered by OLMCTS and hope.

Controller description: essex_acwebb

First Version

Controller description: Number27

Using a GA for local movement and a value map to direct the player across the level. The value map is created by evaluating each object type which results in a certain influence across the map. Notable events, the time spent in one area or non deterministic movements influence the players behaviour or how it chooses its optimal action.

Sample Rolling Horizon Evolutionary Algorithm

Controller description: SpaceJohn_Team

SpaceJohn_Team v2

Controller description: Damorin

This AI uses an ensemble decision system to take into account various factors or "Opinions" when selecting an action. The voices are broken down into three different ranges, long, medium and short range. The Long and Short range algorithms are custom built, where as the mid range voice is built upon the sampleMCTS algorithm that was provided.